Dr. Leanne Archer is a Researcher in the School of Geographical Sciences at the University of Bristol, specializing in hydrology and climate change impacts. Her work focuses on flood risk assessment in Small Island Developing States, extreme rainfall events, and the application of convection-permitting climate models. She collaborates with experts like Prof. Paul Bates and Dr. Jonty Rougier on interdisciplinary projects addressing tropical cyclone hazards and climate adaptation strategies. Her research interests include analyzing flood exposure in vulnerable regions, soil moisture dynamics in urban flooding, and improving global flood forecasts for humanitarian operations. Archer’s publications emphasize the implications of climate change on rainfall-driven disasters, particularly in Puerto Rico and East Africa, using high-resolution climate projections to evaluate future risks under 1.5° C and 2° C warming scenarios. Recent work explores innovations like the Surface Water Ocean Topography Mission for flood modeling and evaluates the suitability of TanDEM-X data for inundation studies in island nations. Her contributions bridge environmental science with policy, aiming to enhance disaster preparedness and resilience in climate-sensitive regions.
Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).
Craig Davis is a Professor and holds the Bollenbach Chair in Wildlife Management at Oklahoma State University's Department of Natural Resource Ecology & Management. He has been a faculty member since 2001, advancing from Assistant Professor (2001-2007) to Associate Professor (2007-2012), and has served as Bollenbach Chair since 2014. Davis earned his PhD in Wildlife Science from Texas Tech University (1996), an MS from Iowa State University (1991), and a BS from Ohio State University (1988). He is also a Certified Wildlife Biologist through The Wildlife Society. His research focuses on avian ecology, wetland restoration, and gamebird management, with specific interests in fire-grazing interactions, invertebrate communities, and climate change impacts on ecosystems. Key areas include grassland bird survival, wetland classification systems, and the effects of anthropogenic development on wildlife habitats. Davis actively collaborates with conservation organizations and agencies, addressing challenges like invasive species management and water infrastructure resilience. Teaching responsibilities include undergraduate courses on Wildlife Management Techniques and Wetland Ecology, as well as graduate courses like Advanced Wetland Ecology. His grants emphasize wetland restoration outreach, climate adaptation strategies, and studies on northern bobwhite and wild turkey populations. Notable projects include evaluating cellular transmission technology in wildlife monitoring and assessing the impact of extreme weather on gamebird survival. Grants (Selected): Expansion of potential wetland restoration opportunities in Oklahoma (2024-2027) Linking Research and Stakeholder Engagement for Wetland Conservation (2024-2026) Effects of Extreme Weather and Hunting on Northern Bobwhite (2023-2025) Research Themes: Aligns with UN Sustainable Development Goals 15 (Life on Land), 13 (Climate Action), 6 (Clean Water), 11 (Sustainable Cities), 14 (Life Below Water), and 9 (Industry/Infrastructure).
Gregg M. Garfin is a Professor and Specialist in Climate, Natural Resources, and Policy at the University of Arizona’s School of Natural Resources and the Environment, and Deputy Director for Science Translation and Outreach at the Arizona Institute for Resilience. He holds a Ph.D. in Geosciences from the University of Arizona (2003), an M.S. in Geology and Geography from the University of Massachusetts (1997), and a B.S. in Geology and Geography from the same institution (1993). His work bridges science and societal decision-making, focusing on climate adaptation, drought management, and climate services. He directs the Water, Society, and Policy M.S. Program and has led interdisciplinary projects on climate resilience, including contributions to the Fourth National Climate Assessment. Roles: Professor, Deputy Director, Program Director Affiliations: Arizona Institute for Resilience, School of Natural Resources and the Environment Research Interests His research emphasizes climate adaptation strategies, climate policy, and the co-production of knowledge between scientists and decision-makers. Key areas include: Climate impacts on water resources and ecosystems Extreme heat and drought resilience in urban and border communities Climate change communication and education Awards & Recognition Garfin has received multiple accolades, including AAAS Fellowship (2020), University of Arizona Service Awards (2020, 2015), and the Global Initiative for Academic Networks (GIAN) Visiting Fellowship (2018). His work has been featured in peer-reviewed journals and media outlets, emphasizing actionable science for climate resilience. Grants & Contributions He has led federal and transnational projects, including binational climate resilience initiatives in the U.S.-Mexico border region and flood risk assessments in Seoul, South Korea. His work integrates participatory governance approaches to enhance community preparedness.
Elmira Hassanzadeh is an Associate Professor at the Department of Civil, Geological and Mining Engineering , Polytechnique Montréal, with a focus on integrated water resource modeling and management under climate and anthropogenic changes. She is affiliated with the Global Institute for Water Security (University of Saskatchewan) and the Experimental and Digital Water Flow Engineering Group (GENIE EAU) . Education: PhD in Civil Engineering, University of Saskatchewan Postdoctoral Fellow, McGill University Research interests include climate change impact assessment, socio-hydrological modeling, stakeholder engagement in water management, and sustainable development of water systems in cold regions. Her work emphasizes decision-making under uncertainty and adaptive strategies for ungauged basins. Recent publications (2024–2020) span coastal flood hazard modeling, climate-driven hydrological changes in tropical and Canadian watersheds, evaluation of agricultural practices under warming climates, and integrated approaches to water quality and system dynamics. Keywords include Hydrology , Climate Change , Water Security , and Socio-Hydrology . Supervision: She has mentored 15+ graduate students in projects related to Lake Urmia, Saskatchewan River Basin, and climate adaptation in Africa and the Pacific.
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Ben Livneh is an Associate Professor at the University of Colorado Boulder , affiliated with both the Civil, Environmental, and Architectural Engineering Department and the Cooperative Institute for Research in Environmental Sciences (CIRES) . As Director of the Western Water Assessment , he bridges academic research with regional climate resilience initiatives. Ph.D. in Civil Engineering (Hydrology), University of Washington (2012) MESc in Civil Engineering, University of Western Ontario (2006) His research explores hydrologic responses to climate and land-cover changes , focusing on snowpack dynamics, wildfire impacts on water quality, sediment transport, and drought predictability. Key projects include simulations of montane snowpack for wolverine habitat preservation and post-fire landslide susceptibility analysis . Recent publications highlight continental-scale hydraulic geometry datasets , climate-energy nexus challenges , and global lake level reconstructions using satellite data. His work has been recognized by the AGU Hydrologic Sciences Early Career Award (2022) and NASA New Investigator Program (2018) . Scientific Awards AGU Hydrologic Sciences Early Career Award (2022) NASA New Investigator Award (2018) Symposium Scholar, DISCCRS VIII (2013) CIRES Visiting Fellowship (2012) Ben leads interdisciplinary collaborations with institutions like the University of Alaska Southeast and NOAA , addressing climate-water-energy-food nexus challenges through advanced modeling and remote sensing techniques.
Mark Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, with a primary appointment in the Whiting School of Engineering. He is also a Fellow of the Hopkins Extreme Materials Institute. His research focuses on developing ultrahigh-speed optical systems at the intersection of photonics and electronics, emphasizing photonic devices and information theory to advance imaging, sensing, and communications technologies. Applications include quantum-optical systems, ultrawide-bandwidth microwave photonics, and terahertz-rate imaging systems. Dr. Foster received his BS (2003), MS (2007), and PhD (2008) in Applied and Engineering Physics from Cornell University. Before joining Johns Hopkins in 2010, he served as a postdoctoral associate there. His work has been funded by the NSF, IARPA, DTRA, and NIH, resulting in over 200 publications and eight patents. He has held leadership roles, including chairing the IEEE Photonics Society’s Baltimore chapter (2011–2014). Research Highlights: World-leading imaging systems achieving terahertz frame rates Quantum-optical platforms and nonlinear photonic materials (e.g., NbTiOx) Secure authentication via physically unclonable functions (PUFs) Applications in fusion energy diagnostics and medical imaging His awards include the NSF CAREER Award (201?), DARPA Young Faculty Award, and ONR Young Investigator Award. Current projects explore machine learning-resistant PUFs, multi-modal imaging systems, and photonics for extreme environments.
Prof. Richard L. Peters is a Professor and Head of the Chair of Tree Growth and Wood Physiology at the Technische Universität München (TUM) since 2024. His research focuses on tree physiology, wood formation, and climate-forest interactions. He holds a doctorate summa cum laude from the University of Basel (2018) and has conducted postdoctoral research at the Swiss Federal Institute for Forest Science (WSL) and Ghent University. His work integrates forest ecology, dendrochronology, and ecophysiology to address climate change impacts on forests. Education: B.Sc./M.Sc. in Biology at Utrecht University, Ph.D. from University of Basel (2018). Key career steps include a Swiss National Science Foundation (SNSF) fellowship at Ghent University and coordination of the Swiss Canopy Crane II project (2021). Research Interests: Tree water-use strategies, drought tolerance mechanisms, carbon allocation dynamics, and the physiological basis of tree growth. He leads interdisciplinary projects to monitor forest responses to environmental stress using tools like the TreeNet network. Awards: Early Postdoc Mobility Fellowship (SNSF, 2019), Doctorate summa cum laude (2018). Contributions: Authored >180 publications, co-developed the datacleanr R package for ecological data processing, and pioneered methods linking tree-ring data with climate models. His work emphasizes the need for better observational data to improve vegetation models.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Amir AghaKouchak is a Chancellor’s Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine (UCI). He directs the Center for Hydrometeorology and Remote Sensing (CHRS) and focuses on interdisciplinary research at the intersection of hydrology, climatology, statistics, and remote sensing. B.Sc. and M.Sc. in Civil Engineering (Water Resources), K.N. Toosi University of Technology, Tehran, Iran (2001, 2005) Ph.D. in Civil and Environmental Engineering, University of Stuttgart, Germany (2010) His research aims to leverage satellite data and ground observations to advance modeling of hydrologic extremes (droughts, floods, landslides) and develop decision-support systems for water resources management. Key areas include climate change impacts , stochastic modeling , and the water-energy nexus . Recent publications highlight his work on multi-hazard analysis, groundwater decline, wildfire resilience, and climate projections. Tools like MhAST and MvCAT demonstrate his methodological contributions to copula-based and multi-hazard modeling.
Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Miyuki Hino is an Assistant Professor in the Department of City and Regional Planning and an Adjunct Assistant Professor in the Environment, Ecology, and Energy Program at the University of North Carolina at Chapel Hill. She holds a Ph.D. in Environment and Resources from Stanford University and a B.S. in Chemical Engineering from Yale University. Her research focuses on climate hazards, governance, and public policy , with emphasis on equitable adaptation to climate change. Key areas include sea level rise impacts, flood risk on property markets, and managed retreat strategies. She has conducted extensive work on floodplain development policies, household relocation programs, and community resilience frameworks. Dr. Hino's interdisciplinary approach integrates environmental science, urban planning, and social equity . She collaborates with academic and municipal partners, such as the Center for Urban and Regional Studies and Annapolis, MD local governments, to develop actionable solutions for climate adaptation. Her work bridges technical analyses (e.g., sensor networks, machine learning) with policy design to ensure both effectiveness and justice in climate responses. Recent projects emphasize preventing future 'trapped households' by analyzing zoning policies and market dynamics that drive risky development. She advocates for climate-smart growth strategies to balance economic needs with environmental safety, while addressing disparities in vulnerability across communities. Her research has been featured in Science Advances , Nature Climate Change , and interdisciplinary journals. She actively engages with policymakers to translate findings into practical measures, such as equitable buyout programs and floodplain management reforms.
Alexis Berne is an Associate Professor at the Environmental Remote Sensing Laboratory (LTE) within the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL). He co-directs the SSIE-GE program and serves as a member of the CDS (Commission for Doctoral Studies) . His research spans radar meteorology, precipitation microphysics, polar precipitation, and geostatistics, with a focus on mountainous and polar regions. Current Positions Associate Professor, LTE, EPFL (2013–present) Co-Director, SSIE-GE, EPFL PhD Program Committee Member, EDCE-GE, EPFL Research Interests include the remote sensing of precipitation, particularly snowfall and ice production mechanisms, using radar and geostatistical methods. His work addresses atmospheric processes in extreme environments like Antarctica and the Swiss Alps, leveraging machine learning and numerical modeling for climate analysis. Teaching encompasses courses on remote sensing, atmospheric processes, and climate change, emphasizing interdisciplinary approaches and spatiotemporal variability. He advises current PhD students such as Heather Anne Corden and Gionata Ghiggi, alongside mentoring past students like Jacopo Grazioli and Timothy Hugh Raupach.
